Pharmacophores methods

Mason JS, Morize 1, Menard PR, Cheney DL, Hulme C, Labaudiniere RF. New 4-point pharmacophore method for molecular similarity and diversity applications Overview of the method and applications, including a novel approach to the design of combinatorial libraries containing privileged substructures. I Med Chem 1999 42 3251-64. 

Table 3 Examples of VS using primarily pharmacophore methods...

Mason JS, Morize I, Menard PR, Cheney DL, Huhne C, Labaudiniere RF. (1999) New 4-Point Pharmacophore Method for Molecular Similarity and Diversity Appheations Overview of the Method and Applications, including a Novel Approach to the Design of Combinatorial Libraries Containing Privileged Substructures. J. Med. Chem. 42 3251-3264. 

The key proteins that have been modeled with such pharmacophore methods include the major cytochrome P450 (CYP) enzymes, UDP-glucuronosyltransfer-... 

Additionally, computational chemists often use the resulting output alignment of the molecules as input for 3D-QSAR modeling. As already stated, most field-based 3D-QSAR approaches (such as CoMFA) need a pre-aligned set of molecules and the pharmacophore method is certainly one of the best ways to obtain an objective alignment of the compounds. Klabunde et al., for instance, have recently reported the use of a pharmacophore model of human liver glycogen phosphorylase inhibitors together with 3D information from inhibitor-enzyme complexes to derive a predictive CoMFA model [98]. 

Many different methods have been developed both to measure diversity and to select diverse sets of compounds, however, currently there is no clear picture of which methods are best. To date, some work has been done on comparing the various methods however, there is a great need for more validation studies to be performed both on the structural descriptors used and on the different compound selection strategies that have been devised. In some cases, the characteristics of the library itself might determine the choice of descriptors and the compound selection methods that can be applied. For example, computationally expensive methods such as 3D pharmacophore methods are limited in the size of libraries that can be handled. Thus for product-based selection, they are currently restricted to handling libraries of tens of thousands of compounds rather than the millions that can be handled using 2D based descriptors. 

A powerful extension to the potential pharmacophore method has been developed, in which one of the points is forced to contain a special pharmacophore feature, as illustrated in figure 4. All the potential pharmacophores in the pharmacophore key must contain this feature, thus making it possible to reference the pharmacophoric shapes of the molecule relative to the special feature. This gives an internally referenced or relative measure of molecular similarity/diversity. The special feature can be assigned to any atom-type or site-point, or to special dummy atoms, such as those added as centroids of privileged substructures [7, 10]. With one of the points being reserved for this special feature, it would seem even more necessary to use the 4-point definition to capture enough of the... 

The 3-point and 4-point pharmacophore methods can be used to analyse and compare different sets of compounds and databases. Figure 7 illustrates the 4-point pharmacophores for the MDDR database [25], the Available Chemical Directory (ACD) [25], a company registry database and a set of combinatorial libraries reported by Mason and co-workers [7, 11, 13]. Previous studies [3] had shown the increase in resolution possible using 4-point instead of 3-point pharmacophores. 

The multiple potential pharmacophore method, used in an absolute or relative sense, provides a powerful new tool for 3D similarity studies. As an example, two endothelin receptor antagonists with about 20 nM activity as antagonists of the ETA receptor were compared [21], Figure 9 shows the numbers of potential 4-point pharmacophores and overlapping pharmacophores. The two compounds have very low 2D similarity, but have significant overlap of their 4-point potential pharmacophores, illustrating the power of the method to find similarity between compounds with similar biological activities. 

The ability of the pharmacophore method to identify and focus on features important for drug-receptor interactions was important for this result for example, the assignment of the acidic feature to the acylsulfonamide group increases the overlap by about a third (acids were also considered as general hydrogen-bond acceptors for this analysis). 

The use of relative similarity and diversity methods can add powerful new methods for design and analysis. The multiple potential pharmacophore method has been described, and its application to practical design problems discussed. These studies highlight the importance of 4-point pharmacophores and the use of special centres to focus diversity studies. 

One of the most prominent reasons for the retrieval of false-positives in pharmacophore screens is the lack of spatial restriction of the models the chances that a compound will have the required features somewhere present in its structure increases with its size and flexibility. Therefore, most 3D pharmacophore methods have the possibility to add either inclusion or shape volumes that must be filled by the ligands, or forbidden or excluded volumes that describe the space that is occupied by atoms forming the binding site. 

Finally, while pharmacophore and docking methods are still two distinct methods for VS, the distance between them appears to be growing smaller structure-based pharmacophore methods are trying to include more and more information about the binding site (Sections 3.4.2 and 3.4.3), while some docking programs have successfully incorporated pharmacophore constraints, which we discuss in the next section. 

Figure 7.12 Bioisosteric replacement of 2-methylpropionic acid in 7.6. These fragments were suggested by SQUIRRELnovo based on shape matching (mesh) and pharmacophore point scoring (LIQUID fuzzy pharmacophore method). All bioisosteres have been proven to exhibit the desired bioactivity as building-blocks for PPAR agonists.

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